Gendered News Coverage and Women as Heads of Government
Bibliographic record
Abstract
Women politicians have long faced a gendered media environment, where their novelty, potential (in)competence, family, and appearance have been over-emphasized in comparison to men. Much of this literature has focused on politicians running for office and women who hold legislative office. Little research investigates gendered news media presentations of women as heads of government. While the literature predicts that women heads of government should experience gendered differences in news coverage, there is also good reason to expect that news about government operations should not vary based on the gender of the government leader. Using their first year of online news coverage (N = 11,675), we build a series of dictionaries and use automated content analysis to assess how frequently heads of government’s uniqueness, gender, family, appearance, sexual orientation, character, and competence are presented. We also assess the tone of news about each head of government. Results show that gendered coverage exists for women heads of government in potentially surprising ways. Fewer new stories are written about them, on average, than men. Women’s coverage features more feminine and masculine gendered identifiers, as well as more coverage about their clothing. We find little evidence for increased personalization, and women’s character and competence are presented more positively than men’s. Though blunt, this analysis shows that news about heads of government remains gendered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".